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Clarifying the Role of Principal Stratification in the Paired Availability Design

Author

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  • Baker Stuart G

    (National Institutes of Health)

  • Lindeman Karen S

    (Johns Hopkins Medical Institutions)

  • Kramer Barnett S

    (National Institutes of Health)

Abstract

The paired availability design for historical controls postulated four classes corresponding to the treatment (old or new) a participant would receive if arrival occurred during either of two time periods associated with different availabilities of treatment. These classes were later extended to other settings and called principal strata. Judea Pearl asks if principal stratification is a goal or a tool and lists four interpretations of principal stratification. In the case of the paired availability design, principal stratification is a tool that falls squarely into Pearl’s interpretation of principal stratification as “an approximation to research questions concerning population averages.” We describe the paired availability design and the important role played by principal stratification in estimating the effect of receipt of treatment in a population using data on changes in availability of treatment. We discuss the assumptions and their plausibility. We also introduce the extrapolated estimate to make the generalizability assumption more plausible. By showing why the assumptions are plausible we show why the paired availability design, which includes principal stratification as a key component, is useful for estimating the effect of receipt of treatment in a population. Thus, for our application, we answer Pearl’s challenge to clearly demonstrate the value of principal stratification.

Suggested Citation

  • Baker Stuart G & Lindeman Karen S & Kramer Barnett S, 2011. "Clarifying the Role of Principal Stratification in the Paired Availability Design," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-11, May.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:25
    DOI: 10.2202/1557-4679.1338
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    References listed on IDEAS

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    1. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. Stuart G. Baker, 2011. "Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome: An Alternative Approach," Biometrics, The International Biometric Society, vol. 67(1), pages 319-323, March.
    3. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    4. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
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    Cited by:

    1. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.

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